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Advancements in machine learning for material design and process optimization in the field of additive manufacturing
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作者 Hao-ran Zhou Hao Yang +8 位作者 Huai-qian Li Ying-chun Ma Sen Yu Jian shi Jing-chang Cheng Peng Gao Bo Yu Zhi-quan Miao Yan-peng Wei 《China Foundry》 SCIE EI CAS CSCD 2024年第2期101-115,共15页
Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co... Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. 展开更多
关键词 additive manufacturing machine learning material design process optimization intersection of disciplines embedded machine learning
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